Although nonnegative matrix factorization (NMF) favors a
part-based and sparse representation of its input, there is no
guarantee for this behavior. Several extensions to NMF have
been proposed in order to introduce sparseness via the l1-
norm, while little work is done using the more natural sparseness
measure, the l0-pseudo-norm. In this work we propose
two NMF algorithms with l0-sparseness constraints on the
bases and the coefficient matrices, respectively. We show
that classic NMF is a suited tool for l0-sparse NMF algorithms,
due to a property we call sparseness maintenance.
We apply our algorithms to synthetic and real-world data and
compare our results to sparse NMF and nonnegative KSVD.